Designing uncertain optimization schemes for the economic assessment of stock energy-efficiency measures

References

• Ahrnens, C., and E. Borglund. 2007. Fukt på kallvindar-En kartläggning av småhus i Västra Götalands län Examensarbete 2007: 11, Institutionen för bygg-och miljöteknik, Chalmers tekniska höskola (In Swedish). http://documents.vsect.chalmers.se/CPL/exjobb2007/ex2007-011.pdfhttp://documents.vsect.chalmers.se/CPL/exjobb2007/ex2007-011.pdf.
   Google Scholar
• Asadi, E., M. G. da Silva, C. H. Antunes, and L. Dias. 2012. “A Multi-Objective Optimization Model for Building Retrofit Strategies Using TRNSYS Simulations, GenOpt and MATLAB.” Building and Environment 56: 370–378. doi: 10.1016/j.buildenv.2012.04.005
   Web of Science ®Google Scholar
• Booth, A., R. Choudhary, and D. Spiegelhalter. 2013. “A Hierarchical Bayesian Framework for Calibrating Micro-Level Models with Macro-Level Data.” Journal of Building Performance Simulation 6: 1–26. doi: 10.1080/19401493.2012.723750
   Web of Science ®Google Scholar
• Das, P., Z. Chalabi, B. Jones, J. Milner, C. Shrubsole, M. Davies, I. Hamilton, I. Ridley, and P. Wilkinson. 2013. “Multi-Objective Methods for Determining Optimal Ventilation Rates in Dwellings.” Building and Environment 66: 72–81. doi: 10.1016/j.buildenv.2013.03.021
   Web of Science ®Google Scholar
• Das, P., E. Nix, Z. Chalabi, M. Davies, C. Shrubsole, and J. Taylor. 2014a. “Exploring the Health/Energy Pareto-Optimal Front for Adapting a Case-Study Dwelling in the Delhi Environment.” Proceedings of building simulation optimization, second IBPSA-England conference in association with CIBSE, UCL, London, June 23–34.
   Google Scholar
• Das, P., C. Shrubsole, B. Jones, I. Hamilton, Z. Chalabi, M. Davies, A. Mavrogianni, and J. Taylor. 2014b. “Using Probabilistic Sampling-Based Sensitivity Analyses for Indoor Air Quality Modelling.” Building and Environment 78: 171–182. doi: 10.1016/j.buildenv.2014.04.017
   Web of Science ®Google Scholar
• Deb, K. 2001. Multi-Objective Optimization Using Evolutionary Algorithms. Vol. 2012. Chichester: John Wiley & Sons.
   Google Scholar
• Fesanghary, M., S. Asadi, and Z. W. Geem. 2012. “Design of Low-Emission and Energy-Efficient Residential Buildings Using a Multi-Objective Optimization Algorithm.” Building and Environment 49 (1): 245–250. doi: 10.1016/j.buildenv.2011.09.030
   Web of Science ®Google Scholar
• Goodacre, C., S. Sharples, and P. Smith. 2002. “Integrating Energy Efficiency with the Social Agenda in Sustainability.” Energy and Buildings 34 (1): 53–61. doi: 10.1016/S0378-7788(01)00077-9
   Web of Science ®Google Scholar
• Hagentoft, C.-E. 2011. “Probabilistic Analysis of Hygrothermal Conditions and Mould Growth Potential in Cold Attics. Impact of Weather, Building System and Construction Design Characteristics.” Proceedings of international conference on durability of building materials and components, Porto, Portugal, April 12–15.
   Google Scholar
• Hagentoft, C.-E., A. S. Kalagasidis, S. Nilsson, M. Thorin, and A. Ventotech. 2008. “ Mould Growth Control in Cold Attics through Adaptive Ventilation.” Proceedings of 8th nordic symposium of building physics, Copenhagen, Denmark, June 16–18.
   Google Scholar
• Hopfe, C. J., G. L. M. Augenbroe, and J. L. M. Hensen. 2013. “Multi-Criteria Decision Making Under Uncertainty in Building Performance Assessment.” Building and Environment 69: 81–90. doi: 10.1016/j.buildenv.2013.07.019
   Web of Science ®Google Scholar
• Hukka, A., and H. A. Viitanen. 1999. “A Mathematical Model of Mould Growth on Wooden Material.” Wood Science and Technology 33 (6): 475–485. doi: 10.1007/s002260050131
   Web of Science ®Google Scholar
• Janssen, H. 2013. “Monte-Carlo Based Uncertainty Analysis: Sampling Efficiency and Sampling Convergence.” Reliability Engineering & System Safety 109: 123–132. doi: 10.1016/j.ress.2012.08.003
   Web of Science ®Google Scholar
• Kuckshinrichs, W., T. Kronenberg, and P. Hansen. 2010. “The Social Return on Investment in the Energy Efficiency of Buildings in Germany.” Energy Policy 38 (8): 4317–4329. doi: 10.1016/j.enpol.2010.03.060
   Web of Science ®Google Scholar
• Malatji, E. M., J. Zhang, and X. Xia. 2013. “A Multiple Objective Optimisation Model for Building Energy Efficiency Investment Decision.” Energy and Buildings 61 (1): 81–87. doi: 10.1016/j.enbuild.2013.01.042
   Web of Science ®Google Scholar
• Nguyen, A.-T., S. Reiter, and P. Rigo. 2014. “A Review on Simulation-Based Optimization Methods Applied to Building Performance Analysis.” Applied Energy 113: 1043–1058. doi: 10.1016/j.apenergy.2013.08.061
   Web of Science ®Google Scholar
• Rysanek, A. M., and R. Choudhary. 2013. “Optimum Building Energy Retrofits Under Technical and Economic Uncertainty.” Energy and Buildings 57: 324–337. doi: 10.1016/j.enbuild.2012.10.027
   Web of Science ®Google Scholar
• Tuhus-Dubrow, D., and M. Krarti. 2010. “Genetic-Algorithm Based Approach to Optimize Building Envelope Design for Residential Buildings.” Building and Environment 45 (7): 1574–1581. doi: 10.1016/j.buildenv.2010.01.005
   Web of Science ®Google Scholar
• Turner, W. J. N., J. M. Logue, and C. P. Wray. 2013. “A Combined Energy and IAQ Assessment of the Potential Value of Commissioning Residential Mechanical Ventilation Systems.” Building and Environment 60: 194–201. doi: 10.1016/j.buildenv.2012.10.016
   Web of Science ®Google Scholar
• Van Gelder, L., P. Das, H. Janssen, and S. Roels. 2014a. “Comparative Study of Metamodelling Techniques in Building Energy Simulation: Guidelines for Practitioners.” Simulation Modelling Practice and Theory 49: 245–257. doi: 10.1016/j.simpat.2014.10.004
   Web of Science ®Google Scholar
• Van Gelder, L., H. Janssen, and S. Roels. 2014b. “Probabilistic Design and Analysis of Building Performances: Methodology and Application Example.” Energy and Buildings 79: 202–211. doi: 10.1016/j.enbuild.2014.04.042
   Web of Science ®Google Scholar